Tuberculosis (TB) is due to infection with the pathogen Mycobacterium tuberculosis (Mtb). This disease represents a global health pandemic as based on WHO statistics it claims the lives of approximately 1.5 million people per year, while infecting nearly 9 million. New drugs are urgently needed with novel mechanisms of action that treat this disease while also addressing an important need to reduce the lengthy course of treatment that is at best 6 months in duration. We have a primary goal of discovering novel classes of antibacterials. (E)-6-(2-((5-nitrofuran-2-yl)methylene)hydrazinyl)-N2,N4-diphenyl-1,3,5-triazine-2,4-diamine (JSF-2019) [1], was rediscovered by us using Bayesian machine learning models in 2013. It represents a class of antitubercular agents reported only once in 1969 [2]. While JSF-2019 did not exhibit in vivo efficacy in an acute model in our hands, a close analog, ((E)-N2,N4-diisopropyl-6-(2-((5-nitrofuran-2-yl)methylene)hydrazinyl)- 1,3,5-triazine-2,4-diamine (JSF-2032) [2] was reported in 1969 to exhibit in vivo activity. Our preliminary data on solubility in PBS, mouse liver microsomal stability, Caco-2 cell permeability, and mouse snapshot pharmacokinetic (PK) profiles demonstrate that the diaminotriazine class of antituberculars holds significant promise for seeding a novel therapeutic. We aim to further improve upon the in vitro efficacy, in vitro Absorption, Distribution, Metabolism and Excretion (ADME) and in vivo pharmacokinetic (PK) profiles of these early compounds. The Specific Aims of this proposed research are: Utilize medicinal chemistry and predictive ADME models to optimize the initial triazine hit family as antitubercular agents. Apply transcriptional profiling nd resistant mutant/whole-genome sequencing methods to identify potential drug targets and mechanism of action of the triazine antitubercular class. Phase I would, therefore, seek to deliver an in vivo active small molecule triazine with information as to potential target/s through complimentary methods. A Phase II program would leverage this information to further optimize this series towards a preclinical candidate of significant interest to foundations and/or biotech/pharmaceutical companies.